CN110707711A - User-side comprehensive energy system hierarchical regulation and control method and system - Google Patents

User-side comprehensive energy system hierarchical regulation and control method and system Download PDF

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CN110707711A
CN110707711A CN201810753152.7A CN201810753152A CN110707711A CN 110707711 A CN110707711 A CN 110707711A CN 201810753152 A CN201810753152 A CN 201810753152A CN 110707711 A CN110707711 A CN 110707711A
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energy
regulation
load
demand
level
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CN110707711B (en
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李�昊
赵宝国
李斌
霍现旭
刘畅
李文
赵新
林晶怡
张静
覃剑
赵洪磊
王剑锋
李国栋
孙龙彪
张筱慧
娄铖伟
李佳馨
吴邦旭
唐巍
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State Grid Corp of China SGCC
China Agricultural University
China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
China Agricultural University
China Electric Power Research Institute Co Ltd CEPRI
State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

A user-side integrated energy system regulation and control method is characterized by comprising the following steps: dividing a regulation period into regulation periods according to the load energy demand of a user side and a time sequence rule; calculating by adopting a fuzzy clustering algorithm based on the regulation and control time interval to obtain energy utilization grades; and regulating and controlling by combining energy value label sequencing of the energy equipment based on the regulating and controlling time period and energy utilization grading. Different grading standards are set under different regulation and control scenes and different regulation and control time periods, so that the grading regulation and control of the comprehensive energy system at the user side are more reasonable and flexible, and the problem that the balance of supply and demand cannot be realized by the generation and distribution of energy is solved.

Description

User-side comprehensive energy system hierarchical regulation and control method and system
Technical Field
The invention relates to comprehensive energy, in particular to a hierarchical regulation and control method and system for a user-side comprehensive energy system.
Background
The comprehensive energy system at the user side is an energy comprehensive network with various energy interaction and complementation. In the operation process of the user-side comprehensive energy system, the balance of supply and demand and the consumption and the stabilization of renewable energy sources cannot be realized for the generation, transmission and distribution, conversion, storage, consumption and transaction of various energy sources. The comprehensive energy system on the user side can manage and control all the units on one level, and can also divide the units into a plurality of energy stations according to the spatial distribution of energy utilization loads and energy supply equipment to perform layered management and control.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a user-side comprehensive energy system hierarchical regulation and control method and system.
The technical scheme provided by the invention is as follows:
a user-side integrated energy system regulation and control method comprises the following steps:
dividing a regulation period into regulation periods according to the load energy demand of a user side and a time sequence rule;
calculating by adopting a fuzzy clustering algorithm based on the regulation and control time interval to obtain energy utilization grades;
and regulating and controlling by combining energy value label sequencing of the energy equipment based on the regulating and controlling time period and energy utilization grading.
Preferably, the dividing the regulation and control cycle into regulation and control periods according to the user side load energy demand and the time sequence rule includes:
judging whether the time of fluctuation of the user side load energy consumption requirement is consistent, if so, dividing the regulation and control time interval according to the time of fluctuation;
otherwise, dividing the regulation and control time period by adopting a fuzzy clustering method.
Preferably, the dividing of the regulation and control period by using the fuzzy clustering method includes:
carrying out normalization processing on the collected user load data to obtain a load curve, and carrying out filtering, moving average, exponential average and curve fitting processing on the load curve;
performing per-unit processing on the processed load curve;
and dividing the load curve subjected to per unit processing into regulation and control time intervals by adopting a fuzzy clustering algorithm.
Preferably, the load curve is subjected to per unit processing as shown in the following formula:
Figure BDA0001726088940000021
in the formula: x is the number oftThe time t value of the load curve is per unit; ptThe load value at the t moment of the original load curve is obtained; pmaxAnd (4) the maximum load value of the load curve.
Preferably, the dividing the load curve subjected to the per-unit processing into regulation and control periods by using a fuzzy clustering algorithm includes:
taking the number of the regulation and control time intervals as the number of clusters, constructing a membership matrix and a cluster center based on the load curve after per unit processing, and performing iterative computation;
dividing adjacent load points belonging to the same clustering center into the same time interval;
the load curve after the per unit processing comprises: and the supply and demand data of cold and heat power.
Preferably, the fuzzy clustering algorithm includes: the objective function is as follows:
Figure BDA0001726088940000022
in the formula: u is a membership matrix constructed based on the load curve after per unit processing; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; dikIth cooling, heating and power supply and demand data sample xiDistance from class k center; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity;
wherein d isikCalculated as follows:
in the formula, m is the characteristic number of the sample; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; and k is the serial number of the clustering center.
Preferably, the membership matrix is constructed according to the following formula:
in the formula uikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; dikData sample x for the ith cooling, heating and power supply and demandiDistance from class k center; r is the number of iterations; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity.
Preferably, the cluster center is constructed according to the following formula:
Figure BDA0001726088940000031
in the formula (I), the compound is shown in the specification,
Figure BDA0001726088940000032
is a clustering center; n is a data sample of cooling, heating and power supply and demand; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; r is the number of iterations; k is a clustering center number; i, serial number of data samples of cooling, heating and power supply and demand; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity.
Preferably, the calculating based on the regulation and control period by using a fuzzy clustering algorithm to obtain the energy use grade comprises:
based on the energy use grading number of the comprehensive energy system in each regulation and control time period as the clustering number, constructing a membership matrix and a clustering center based on the load curve after per unit processing, and performing iterative computation;
and grading the energy utilization which belongs to the same clustering center and is used as the comprehensive energy system.
Preferably, the energy usage classification of the integrated energy system includes: a base stage, an intermediate stage and a peak shaving stage;
the basic level is as follows: a portion not greater than a given first energy level on the per-unit processed load curve in each of the regulation and control periods;
the base peak shaving level is as follows: a part greater than a given second performance level on the per-unit processed load curve in each of the regulation periods;
the remaining part is the peak shaver stage.
Preferably, the energy utilization grading based on the regulation time period and the regulation combined with the energy value label sequencing of the energy equipment comprises the following steps:
acquiring an output priority sequence of the energy equipment based on the energy value label sequencing;
and carrying out energy supply and demand balance regulation and control through the output priority sequence, the regulation and control time period and the energy utilization grading of the energy equipment.
Preferably, the obtaining of the priority order of the output of the energy device based on the energy value tag sorting includes:
converting the energy equipment into quantitative indexes based on the sequencing sequence of the energy value labels, and sequencing the energy equipment from large to small according to the quantitative indexes;
calculating the sequencing weighted output level of the energy equipment according to the output level and the quantitative index of the energy equipment;
and sequencing the output priority sequence of the energy equipment according to the sequencing weighted output level of the energy equipment from large to small.
Preferably, the energy supply and demand balance control is performed through the priority order of output of the energy device, the control time period and the energy utilization grading, and the method comprises the following steps:
regulating and controlling the load demand of the basic level through the output priority sequence of the energy equipment;
after the load requirements of the base level are regulated, regulating and controlling the load requirements of the middle level by energy equipment with residual capacity in the energy equipment according to the output priority sequence of the energy equipment;
and after the load requirement of the middle level is regulated, the load requirement of the peak regulation level is balanced by adopting the peak regulation capacity of the reserved peak regulation equipment, and/or the load requirement of the peak regulation level is regulated and controlled by adopting energy equipment with residual capacity according to the output priority sequence of the energy equipment.
A user-side integrated energy system hierarchical regulation system, the system comprising:
a dividing module: the system is used for dividing the regulation period into regulation time periods according to the load energy demand of the user side and the time sequence rule;
a grading module: the comprehensive energy resource system is used for setting different grading standards by adopting a fuzzy clustering method in the regulation and control time period and grading the comprehensive energy resource system;
a regulation module: and the system is used for performing hierarchical supply and demand balance regulation and control on the comprehensive energy system according to the obtained energy value label sequence of the energy equipment in the regulation and control time period.
Preferably, the dividing module includes:
a judgment submodule: the energy consumption judging module is used for judging whether the user side load energy consumption requirement has a remarkable characteristic or not;
a first molecular dividing module: used for dividing regulation and control time intervals according to the significant characteristics;
a second partitioning submodule: the method is used for dividing the regulation and control time period by adopting a fuzzy clustering method.
Preferably, the grading module includes:
a sorting submodule: the power utilization requirement level is decomposed according to a fuzzy clustering method, and power values corresponding to the power utilization requirement level are sequenced;
grading submodule: and grading the energy utilization requirement of the comprehensive energy system at the user side according to the energy utilization requirement level.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a user-side comprehensive energy system regulation and control method which is characterized by comprising the following steps: dividing a regulation period into regulation periods according to the load energy demand of a user side and a time sequence rule; calculating by adopting a fuzzy clustering algorithm based on the regulation and control time interval to obtain energy utilization grades; and regulating and controlling by combining energy value label sequencing of the energy equipment based on the regulating and controlling time period and energy utilization grading. The technical scheme provided by the invention is simple to operate, and the energy regulation and control are reasonable and flexible.
2. The technical scheme provided by the invention is reasonable and feasible, realizes balance of supply and demand, meets the energy demand of users, and simultaneously considers the economic benefits of all benefit subjects and the national energy strategy.
Drawings
FIG. 1 is a flow chart of a hierarchical regulation and control method for a user-side integrated energy system according to the present invention;
FIG. 2 is a schematic diagram of a typical daily load curve of a scientific and technological park according to the present invention;
FIG. 3 is a schematic diagram of typical daily regulation and control period division of a scientific and technological park according to the present invention;
FIG. 4 is a schematic view of the load curve smoothing process of the present invention;
FIG. 5 shows the typical daily load per unit curve regulation period division result of the present invention;
FIG. 6-1 is a schematic diagram of the time-phased regulation grading result of a typical daily load curve of the electrical load of the present invention.
FIG. 6-2 is a schematic diagram of the time-phased regulation grading result of a typical daily load curve of the heat load of the present invention.
Fig. 6-3 are schematic diagrams of the time-interval regulation and classification results of typical daily load curves of the cold load of the invention.
In the figure: 1 is the cold load; 2 is an electrical load; 3 is the heat load; 4 is the peak shaver load demand; 5 is the mid-stage load demand: base level load demand is 6.
Detailed Description
The first embodiment is as follows:
as shown in fig. 1, a method for regulating and controlling a user-side integrated energy system includes:
dividing a regulation period into regulation periods according to the load energy demand of a user side and a time sequence rule;
calculating by adopting a fuzzy clustering algorithm based on the regulation and control time interval to obtain energy utilization grades;
and regulating and controlling by combining energy value label sequencing of the energy equipment based on the regulating and controlling time period and energy utilization grading.
Preferably, the dividing the regulation and control cycle into regulation and control periods according to the user side load energy demand and the time sequence rule includes:
judging whether the time of fluctuation of the user side load energy consumption requirement is consistent, if so, dividing the regulation and control time interval according to the time of fluctuation;
otherwise, dividing the regulation and control time period by adopting a fuzzy clustering method.
Preferably, the dividing of the regulation and control period by using the fuzzy clustering method includes:
carrying out normalization processing on the collected user load data to obtain a load curve, and carrying out filtering, moving average, exponential average and curve fitting processing on the load curve;
performing per-unit processing on the processed load curve;
and dividing the load curve subjected to per unit processing into regulation and control time intervals by adopting a fuzzy clustering algorithm.
Preferably, the load curve is subjected to per unit processing as shown in the following formula:
Figure BDA0001726088940000061
in the formula: x is the number oftThe time t value of the load curve is per unit; ptThe load value at the t moment of the original load curve is obtained; pmaxAnd (4) the maximum load value of the load curve.
Preferably, the dividing the load curve subjected to the per-unit processing into regulation and control periods by using a fuzzy clustering algorithm includes:
taking the number of the regulation and control time intervals as the number of clusters, constructing a membership matrix and a cluster center based on the load curve after per unit processing, and performing iterative computation;
dividing adjacent load points belonging to the same clustering center into the same time interval;
the load curve after the per unit processing comprises: and the supply and demand data of cold and heat power.
Preferably, the fuzzy clustering algorithm includes: the objective function is as follows:
Figure BDA0001726088940000062
in the formula: u is a membership matrix constructed based on the load curve after per unit processing; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; dikIth cooling, heating and power supply and demand data sample xiDistance from class k center; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity;
wherein d isikCalculated as follows:
Figure BDA0001726088940000071
in the formula, m is the characteristic number of the sample; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; and k is the serial number of the clustering center.
Preferably, the membership matrix is constructed according to the following formula:
Figure BDA0001726088940000072
in the formula uikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; dikData sample x for the ith cooling, heating and power supply and demandiDistance from class k center; r is the number of iterations; b is a weighting parameterThe value range is more than or equal to 1 and less than or equal to infinity.
Preferably, the cluster center is constructed according to the following formula:
Figure BDA0001726088940000073
in the formula (I), the compound is shown in the specification,
Figure BDA0001726088940000074
is a clustering center; n is a data sample of cooling, heating and power supply and demand; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; r is the number of iterations; k is a clustering center number; i, serial number of data samples of cooling, heating and power supply and demand; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity.
Preferably, the calculating based on the regulation and control period by using a fuzzy clustering algorithm to obtain the energy use grade comprises:
based on the energy use grading number of the comprehensive energy system in each regulation and control time period as the clustering number, constructing a membership matrix and a clustering center based on the load curve after per unit processing, and performing iterative computation;
and grading the energy utilization which belongs to the same clustering center and is used as the comprehensive energy system.
Preferably, the energy usage classification of the integrated energy system includes: a base stage, an intermediate stage and a peak shaving stage;
the basic level is as follows: a portion not greater than a given first energy level on the per-unit processed load curve in each of the regulation and control periods;
the base peak shaving level is as follows: a part greater than a given second performance level on the per-unit processed load curve in each of the regulation periods;
the remaining part is the peak shaver stage.
Preferably, the energy utilization grading based on the regulation time period and the regulation combined with the energy value label sequencing of the energy equipment comprises the following steps:
acquiring an output priority sequence of the energy equipment based on the energy value label sequencing;
and carrying out energy supply and demand balance regulation and control through the output priority sequence, the regulation and control time period and the energy utilization grading of the energy equipment.
Preferably, the obtaining of the priority order of the output of the energy device based on the energy value tag sorting includes:
converting the energy equipment into quantitative indexes based on the sequencing sequence of the energy value labels, and sequencing the energy equipment from large to small according to the quantitative indexes;
calculating the sequencing weighted output level of the energy equipment according to the output level and the quantitative index of the energy equipment;
and sequencing the output priority sequence of the energy equipment according to the sequencing weighted output level of the energy equipment from large to small.
Preferably, the energy supply and demand balance control is performed through the priority order of output of the energy device, the control time period and the energy utilization grading, and the method comprises the following steps:
regulating and controlling the load demand of the basic level through the output priority sequence of the energy equipment;
after the load requirements of the base level are regulated, regulating and controlling the load requirements of the middle level by energy equipment with residual capacity in the energy equipment according to the output priority sequence of the energy equipment;
and after the load requirement of the middle level is regulated, the load requirement of the peak regulation level is balanced by adopting the peak regulation capacity of the reserved peak regulation equipment, and/or the load requirement of the peak regulation level is regulated and controlled by adopting energy equipment with residual capacity according to the output priority sequence of the energy equipment.
The second embodiment is as follows:
a user-side integrated energy system hierarchical regulation system, the system comprising:
a dividing module: the system is used for dividing the regulation period into regulation time periods according to the load energy demand of the user side and the time sequence rule;
a grading module: the comprehensive energy resource system is used for setting different grading standards by adopting a fuzzy clustering method in the regulation and control time period and grading the comprehensive energy resource system;
a regulation module: and the system is used for performing hierarchical supply and demand balance regulation and control on the comprehensive energy system according to the obtained energy value label sequence of the energy equipment in the regulation and control time period.
Preferably, the dividing module includes:
a judgment submodule: the energy consumption judging module is used for judging whether the user side load energy consumption requirement has a remarkable characteristic or not;
a first molecular dividing module: used for dividing regulation and control time intervals according to the significant characteristics;
a second partitioning submodule: the method is used for dividing the regulation and control time period by adopting a fuzzy clustering method.
Preferably, the grading module includes:
a sorting submodule: the power utilization requirement level is decomposed according to a fuzzy clustering method, and power values corresponding to the power utilization requirement level are sequenced;
grading submodule: and grading the energy utilization requirement of the comprehensive energy system at the user side according to the energy utilization requirement level.
Detailed description of the preferred embodiment
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
The invention discloses a method for controlling the balance of cold/heat/electricity supply and demand, which is characterized in that a technology park mainly comprising various high and new technology research and development mechanisms, service mechanisms and the like is a typical scene of a comprehensive energy system at a user side, and day-ahead scheduling is typical optimized regulation and control content.
1 energy device and load characteristics in a user-side integrated energy system
(1) Gas energy supply equipment
The gas turbine uses natural gas as fuel, the generated power supplies the power demand of users, and the waste heat discharged after the system generates power supplies heat and cold to the users through waste heat recycling equipment. Power generation of gas turbine is PgtThe heating power is alphahPgtRefrigeration power of alphacPgt. The gas turbine can realize combined supply of cold/heat/electricity, and has higher energy supply efficiency.
The heat supply power of the gas boiler is PGB. The gas boiler is suitable for high-power central heating.
(2) Intermittent power generation device
The photovoltaic power generation device has the power generation power of
Figure BDA0001726088940000101
And the installation modes such as the illumination intensity at the moment t, the azimuth angle of the photovoltaic cell panel, the inclination angle and the like are related. The output of the photovoltaic power generation device has the time sequence and randomness.
(3) Energy conversion device
Rated power of the heat pump is PEHPower consumption is betaePEHThe heat generation power is betahPEHCold producing power of betacPEH. The heat pump has a high energy supply efficiency.
The refrigerating power of the electric refrigerating compressor is PECThe power consumption is gammaECPEC。
Cold power P of lithium bromide refrigeratorHCHeat consumption of deltaHCPHC。
(4) Energy storage device
The electric energy stored by the electric energy storage equipment in the system at the moment t is EtThe charging power and the discharging power of the electrical storage device are respectivelyAnd
Figure BDA0001726088940000103
the heat energy stored by the energy storage equipment in the system at the moment t is HtThe heat storage power and the heat release power of the heat storage device are respectively
Figure BDA0001726088940000104
And
Figure BDA0001726088940000105
the cold energy stored by the energy storage equipment in the system at the moment t is CtThe cold storage power and the cold discharge power of the cold storage equipment are respectively
Figure BDA0001726088940000106
And
Figure BDA0001726088940000107
(5) energy consumption requirement
The energy consumption requirements of the scientific and technological park show great difference in different seasons, the demands of cold load and electric load are great in summer working condition, and the demands of heat load are relatively small; the heat load and the electric load demand are large in winter working condition, and the cold load demand is relatively small; under the working conditions of spring and autumn, the demand of electric load is large, and the demand of cold and hot load is relatively small. The daily load curve shows that the energy consumption demand is large during working hours in the daytime, and the overtime phenomenon is common, so that the energy consumption demand is high at night and the energy consumption demand is small at night. The energy demand of users is greatly influenced by factors such as meteorological conditions and the like, and the energy demand is obviously increased in extreme weather. The use of energy-side "electrical energy replacement" can shift some of the cold and heat loads to electrical loads, with the cold and heat demands decreasing and the electricity demands increasing. Fig. 2 is a typical daily load curve diagram of the scientific park.
Method for dividing regulation and control time period in regulation and control cycle of 2-user-side integrated energy system
The grading standard of grading regulation and control of the user-side comprehensive energy system considering the balance of cold/heat/electricity supply and demand is adapted to the energy utilization load level, and different grading standards are adopted in different energy utilization scenes. When the regulation and control cycle is one day, if the daily energy consumption characteristic has a large peak-valley difference, the satisfactory optimization effect cannot be obtained by adopting the uniform grading standard for grading regulation and control, so that the regulation and control cycle is required to be divided into a plurality of regulation and control time intervals according to the energy consumption characteristic, and different grading standards are adopted in each time interval.
(1) Dividing regulation and control time interval according to obvious energy utilization time sequence characteristics
If the energy consumption rate of each user in the garden is high, the time of fluctuation of the energy consumption level is relatively consistent, so that the time when the energy consumption level is concentrated to fluctuate can be used as a control time interval dividing point.
As shown in fig. 2, the daily energy load curve in the summer scene of the scientific and technological park is that at the working start time of 8-9 am, the electric/cold/heat load is obviously increased, and the electric/cold/heat load is at a higher level during the working time of day; the time from 17 to 18 hours is the off-duty time, but the load level is still high at 18 to 22 hours due to the overtime work of science and technology developers; the level of loading does not return to the low level of rest time after 24 hours. Since the load of the park has an obvious time sequence rule, the regulation and control period of 24 hours can be divided into three regulation and control periods of 0 hour-8 hours, 8 hours-17 hours and 17 hours-24 hours, as shown in fig. 3.
(2) Dividing regulation and control time interval by adopting fuzzy clustering method
If the energy utilization rates of all users in the garden are dispersed, and the time sequence consistency of the load energy utilization requirements is not obvious enough, a fuzzy clustering method can be adopted to seek the demarcation point for regulating and controlling time interval division.
Clustering is a process of dividing a data set into a plurality of subsets according to similarity among data, so that data objects in the same set have higher similarity, and the similarity of the data objects in different sets is very small or dissimilar. The fuzzy C-means clustering (FCM) algorithm is integrated with a fuzzy set concept, has high operation speed, can effectively process a large data set, and is very successfully applied in many fields.
1) Load curve preprocessing
When the load curve has obvious short-term fluctuation (such as the original load curve in fig. 4), the fluctuation can interfere the fuzzy clustering analysis effect, so that the influence of the fluctuation needs to be removed in advance, and only the change rule of the load level (such as the smooth curve in fig. 4) is extracted.
The load curve preprocessing method can be realized by adopting methods such as filtering, moving average, exponential average, curve fitting and the like.
2) Load curve per unit processing
The load of the garden is numerous, the energy utilization types are various, the load levels are different, the clustering analysis is carried out under the condition that the dimensions and the magnitude are not uniform, and an ideal result is often difficult to obtain. Therefore, the preprocessed load curve needs to be subjected to per unit processing, and the load values participating in clustering are compressed to a [0,1] closed space to remove the influence of dimension and order difference.
With maximum load value P of load curve participating in clusteringmaxPerforming per unit processing for a reference value:
Figure BDA0001726088940000121
in the formula: x is the number oftThe time t value of the load curve is per unit; ptThe load value at the t-th moment of the original load curve is obtained.
3) Dividing regulation and control time interval by fuzzy clustering algorithm
The fuzzy C-means clustering algorithm is as follows:
let n data samples be X ═ X1,x2,…,xnThen the objective function J can be expressed by:
Figure BDA0001726088940000122
wherein the content of the first and second substances,
Figure BDA0001726088940000123
the constraint conditions are as follows:
Figure BDA0001726088940000124
in the formula: u is a membership matrix, { v }1,v2,…vcThe cluster centers of various types are obtained; c (c is more than or equal to 1 and less than or equal to n) is the number of clustering centers; u. ofk(xi) Is a sample xiFor class AkDegree of membership (abbreviated as u)ik)，dikIs Euclidean distance and is used for measuringi samples xiAnd b is a weighting parameter, the value range is more than or equal to 1 and less than or equal to infinity, and m is the characteristic number of the sample.
Equations (5) and (6) are used to calculate the sample x, respectivelyiFor class AkDegree of membership u ofikAnd optimal clustering center { vi}：
Figure BDA0001726088940000132
Setting a clustering number c during clustering calculation, initializing a membership matrix and a clustering center, setting r as 0, and calculating an objective function J according to a formula (2); iteratively updating the membership degree matrix U and the clustering center v by using an equation (5) and an equation (6), wherein r is r +1 every time iteration is performed; according to given thresholds p and | J(r+1)-J(r)And the magnitude relation of | judges whether to stop iteration. And finishing the iteration process, realizing the cluster analysis of the samples and obtaining the cluster center. And classifying all the data according to the maximum membership degree to obtain the class of the load value at each moment. If the ith data is classified into the jth class, the following formula is required to be satisfied:
the clustering result has no time sequence characteristic, and the load curve has time sequence, so that the load values need to be arranged according to the appearance time sequence, and the adjacent load points belonging to the same clustering center are divided into the same time interval.
Taking 4 typical loads in a certain park as an example, a daily load curve is divided into 5 sections by a fuzzy clustering method, and the control period demarcation points are marked in figure 5.
Energy utilization demand grading method for 3-user-side comprehensive energy system
The fuzzy clustering algorithm is utilized to divide the load energy demand into three levels of a basic level, a middle level and a peak regulation level.
(1) Decomposing energy demand level by adopting fuzzy clustering method
The clustering number c is set to be 3, the number m of data in the sample curve is set, and the regulation and control period is 24 hours per day. If the sampling period is 1 hour, the number m of the data is 24; if the sampling period is 15 minutes, the number of data m becomes 96. Calculating a clustering center A, B, C by using a clustering algorithm; the load energy demand level for one day is broken down into A, B, C levels.
As shown in fig. 2, a typical daily load curve of a scientific and technological park is subjected to clustering analysis by using c-3 and m-96, and the power demand can be decomposed into three levels of 884kW, 1720kW and 1960 kW; decomposing into 647kW, 772kW and 886kW levels by using heat demand; the cold demand is decomposed into three levels of 1065kW, 1196kW and 1721 kW.
As can be seen from fig. 2, the electricity and cold loads of the park have large fluctuation within one day, and if the regulation and control are performed in a grading manner according to the load requirements all day, the utilization of equipment is insufficient, and the regulation and control effect is not ideal, so that the regulation and control period should be segmented. And dividing the regulation and control period of 96 data points counted in 24 hours into three regulation and control time periods of 0 hour-8 hours, 8 hours-17 hours and 17 hours-24 hours, respectively carrying out cluster analysis, and respectively carrying out load energy utilization demand level decomposition.
In the first time period from 0 to 8, clustering analysis is carried out by adopting c to 3 and m to 33, and the power consumption can be decomposed into three levels of 752kW, 956kW and 1104 kW; decomposing into three levels of 616kW, 663kW and 695kW by using heat demand; the cold demand is decomposed into three levels of 934kW, 1183kW and 1430 kW.
In the second time period from 8 hours to 17 hours, clustering analysis is carried out by adopting c-3 and m-37, and the power demand can be decomposed into three levels of 1481kW, 2024kW and 2166 kW; decomposing into 816kW, 868kW and 850kW levels by using heat demand; the cold demand is decomposed into three levels of 1490kW, 1810kW and 1990 kW.
In a third time period from 17 th to 24 th, clustering analysis is carried out by using c-3 and m-26, and the power demand can be decomposed into three levels of 1527kW, 1744kW and 1967 kW; decomposing into three levels of 702kW, 786kW and 852kW by using heat demand; the cold demand is decomposed into three levels of 1105kW, 1350kW and 1482 kW.
(2) User-side energy utilization demand grading for integrated energy system
The load energy demand level of a regulation period is divided into A, B, C three levels, and the maximum value of load power is set as PmaxPower value of level A is PAPower value of level B is PBPower value of level C is PCAnd P isA<PB<PCThen by the power value PA、PBAs a classification standard of the energy demand for the period, the energy power for the load is not more than PAPart is base level load demand, PB-PAPart is the intermediate load demand, Pmax-PBAnd part is the peak shaver level load demand.
Typical daily load curves of the scientific and technological park as shown in fig. 2 show grading results of electricity, heat and cold load demands in each regulation and control period as shown in fig. 6-1 to 6-3.
Therefore, the energy consumption requirement of the park is in the first time period of 0-8 hours, the electric energy requirement 673-; the electric energy requirement of the middle level is 752-956kW, the heat energy requirement is 616-663kW, and the cold energy requirement is 934-1183W; the electric energy requirement of the peak regulation stage is 956-.
In the second time period from 8 hours to 17 hours, the electric energy requirement of the basic level is 1238-; the electric energy requirement of the middle level is 1481-2024kW, the heat energy requirement is 816-868kW, and the cold energy requirement is 1490-1810 kW; the peak regulation level has the electric energy requirement of 2024-.
In the third period from 17 to 24, the electric energy requirement of the basic level is 1440-1527kW, the heat energy requirement is 680-702kW, and the cold energy requirement is 1075-1105 kW; the electric energy requirement of the middle level is 1527 + 1744kW, the heat energy requirement is 702 + 786kW, and the cold energy requirement is 1105 + 1350 kW; the peak regulation level has the electric energy requirement of 1744-.
Supply and demand balancing method for 4-user-side integrated energy system based on energy value label sequencing
Under different energy utilization scenes of a smart city user side comprehensive energy system, comprehensive energy efficiency sequencing of energy equipment is inconsistent, and regulation and control targets are inconsistent, so that specific energy value label sequencing results need to be obtained according to different application scenes and optimization targets. And acquiring an energy equipment output priority sequence based on the energy value label sequencing, and performing supply and demand balance analysis according to the energy equipment output priority sequence and an energy demand grading result in a regulation time period so as to realize the grading regulation and control of the user-side comprehensive energy system.
(1) Control method for obtaining output priority of energy equipment based on energy value label sequencing
In a specific scene, energy devices in the user-side integrated energy system have a sorting order based on the energy value labels, and the priority order needs to be converted into a computable quantitative index.
Setting the sequencing weight of equipment s in the user-side comprehensive energy system under the scene of t time as wt.sAnd the weight coefficient is less than or equal to 1, the value is determined by a sorting sequence, the sorting weight of the first device in the sorting is 1, the sorting weights of other devices are decreased according to the sorting sequence, and the sorting weight is smaller the later the sequence is.
Setting the rated power of equipment s in the comprehensive energy system at the user side as
Figure BDA0001726088940000151
The actual force at time t is
Figure BDA0001726088940000152
The output level (i.e., output power per unit) of the device is
Figure BDA0001726088940000153
Considering the energy value tag ranking, the device's ranked weighted output level is
Figure BDA0001726088940000154
According to the energy equipment output priority control strategy based on energy value label sorting, the larger the sum of the sorting weighted output levels of all the energy equipment of the user-side comprehensive energy system is, the more sufficient the equipment utilization at the front sorting is indicated, so that the regulation and control target can be set to maximize the sorting weighted output levels of the energy equipment, namely:
Figure BDA0001726088940000155
the per unit form is as follows:
Figure BDA0001726088940000161
in the formula: vE、VE *Representing the energy equipment sequencing weighted output sum and the per unit value thereof based on the energy value label sequencing;
Figure BDA0001726088940000162
represents the power rating (kW) of the s-th equipment in the system;
Figure BDA0001726088940000163
representing the actual output (kW) at the moment of the s-th plant t in the system; w is at.sAnd representing the sorting weight of the s-th device in the system under the scene of the t moment.
And adding the output priority control target of the equipment shown in the formula (8) or the formula (9) into the optimization regulation and control target of the user-side comprehensive energy system, and constructing multi-target control, so that the operation optimization control of the comprehensive energy system considering the energy value label sequencing can be realized.
(2) Grading energy supply and demand balance analysis method
The invention adopts a hierarchical regulation strategy to perform hierarchical regulation from three levels, namely a basic level, a middle level and a peak regulation level, so that supply and demand balance also needs to be divided into three levels and balance analysis is performed respectively.
1) Fundamental level energy supply and demand balance analysis
The load demand of the base level is large in quantity and long in duration, so that the energy equipment in the system should be ranked according to the energy value labels of the energy equipment, and the energy equipment ranked in the front is preferentially used for supplying energy.
When the user-side comprehensive energy system consists of a plurality of energy stations and an internet, the supply and demand balance in the energy stations should be preferentially carried out in principle, and the mutual aid among the energy stations is increased if necessary. Because the peak level load has short demand duration and small demand, the scheme of supplying energy by energy equipment which is sequenced at the back in the station is adopted, and compared with the scheme of supplying energy by mutual aid between stations, the economic benefit of the peak level load is better. Thus, it may be considered to reserve a certain device capacity P in the energy stationAC＝Pmax-PBFor peak regulation. The energy equipment with reserved peak regulation capacity should satisfy two conditions: the output adjusting speed is enough to meet the requirement of rapid fluctuation of peak load; and the energy value label is arranged at the last position, and the output priority is controlled to be the lowest. Therefore, the maximum regulation and control capability of the basic level regulation and control equipment in the energy station provides support for the difference between the maximum output allowed by the equipment and the peak regulation capacity, and other energy stations are required when the regulation and control capability is insufficient. Thus, the fundamental level of supply and demand balance within an energy plant can be expressed as:
Figure BDA0001726088940000164
in the formula:
Figure BDA0001726088940000165
representing the base level load requirement of the jth energy station at the t moment in the current regulation and control time interval;
Figure BDA0001726088940000167
representing the maximum output limit of the s-th equipment in the jth energy station at the t moment in the current regulation and control time period;
Figure BDA0001726088940000166
representing the reserved peak regulation capacity of the jth energy station in the current regulation and control time period;
Figure BDA0001726088940000171
representing the basic level energy supply power interacted by other energy stations at the t moment of the jth energy station in the current regulation and control periodAnd the positive value of the rate indicates that the jth energy station absorbs power from other energy stations, and the negative value indicates that the jth energy station provides power to other energy stations.
The balance of supply and demand of the user-side comprehensive energy system at the current regulation and control time interval base level can be expressed as follows:
Figure BDA0001726088940000172
in the formula: k represents the number of seats of the energy station included in the user-side integrated energy system.
2) Intermediate stage energy supply and demand balance analysis
The quantity and duration of the load demand of the middle level are generally less than those of the basic level, and therefore the energy value labels of the energy devices of the system are ranked, and the energy devices which have output capacity in the top ranked system are used for supplying power preferentially.
After the basic level supply and demand balance is completed, part of equipment reaches the output limit and does not participate in the intermediate level supply and demand balance; some devices have power output, and only the residual capacity participates in the supply and demand balance of the intermediate stage. While still reserving peak shaver capacity. Thus, the inter-stage equilibrium supply and demand within an energy station can be expressed as:
Figure BDA0001726088940000173
in the formula:
Figure BDA0001726088940000174
representing the intermediate-level load requirement of the jth energy station at the t moment in the current regulation and control time interval;
Figure BDA0001726088940000175
and the intermediate-level energy supply power interacted by other energy source stations at the time t of the jth energy source station in the current regulation and control period is represented.
The intermediate-level supply and demand balance of the user-side comprehensive energy system in the current regulation and control period can be expressed as follows:
3) peak-shaving level energy supply and demand balance analysis
The peak regulation level load demand is less, the duration is short, the fluctuation speed is fast, and from the angle of peak regulation economy, it can be better to adopt the interior supply and demand balanced mode benefit of station.
After the base-level and intermediate-level supply and demand balance is completed, the peak shaving capacity of the reserved peak shaving equipment can be used for balancing the load of the peak shaving level, and the residual capacity of other equipment can also participate in the peak shaving level supply and demand balance. Thus, the peak shaver supply-demand balance within the energy station can be expressed as:
Figure BDA0001726088940000177
or
Figure BDA0001726088940000181
In the formula:
Figure BDA0001726088940000182
and the peak load regulation requirement of the jth energy station at the moment t in the current regulation and control period is represented.
And respectively carrying out supply and demand balance analysis at different regulation and control time periods according to the hierarchical regulation and control method.
In summary, the invention divides the regulation cycle into appropriate regulation periods according to the demand of load energy consumption and the time sequence rule thereof, sets different grading standards by using a fuzzy clustering method in each regulation period, decomposes the load energy consumption into three different grades, namely a basic grade, a middle grade and a peak regulation grade, obtains a corresponding grading load curve, carries out grading regulation and control based on energy value label sequencing in each regulation period, and carries out supply and demand balance analysis.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (16)

1. A user-side integrated energy system regulation and control method is characterized by comprising the following steps:
dividing a regulation period into regulation periods according to the load energy demand of a user side and a time sequence rule;
calculating by adopting a fuzzy clustering algorithm based on the regulation and control time interval to obtain energy utilization grades;
and regulating and controlling by combining energy value label sequencing of the energy equipment based on the regulating and controlling time period and energy utilization grading.
2. The method for regulating and controlling the comprehensive energy system at the user side according to claim 1, wherein the dividing of the regulation and control period according to the energy demand of the load at the user side and the time sequence law comprises:
judging whether the time of fluctuation of the user side load energy consumption requirement is consistent, if so, dividing the regulation and control time interval according to the time of fluctuation;
otherwise, dividing the regulation and control time period by adopting a fuzzy clustering method.
3. The method as claimed in claim 2, wherein the dividing the regulation period by the fuzzy clustering method comprises:
carrying out normalization processing on the collected user load data to obtain a load curve, and carrying out filtering, moving average, exponential average and curve fitting processing on the load curve;
performing per-unit processing on the processed load curve;
and dividing the load curve subjected to per unit processing into regulation and control time intervals by adopting a fuzzy clustering algorithm.
4. The method according to claim 3, wherein the load curve is per unit processed as shown in the following formula:
Figure FDA0001726088930000011
in the formula: x is the number oftThe time t value of the load curve is per unit; ptThe load value at the t moment of the original load curve is obtained; pmaxAnd (4) the maximum load value of the load curve.
5. The method according to claim 3, wherein the dividing the load curve into the control periods by the fuzzy clustering algorithm comprises:
taking the number of the regulation and control time intervals as the number of clusters, constructing a membership matrix and a cluster center based on the load curve after per unit processing, and performing iterative computation;
dividing adjacent load points belonging to the same clustering center into the same time interval;
the load curve after the per unit processing comprises: and the supply and demand data of cold and heat power.
6. The method as claimed in claim 5, wherein the fuzzy clustering algorithm comprises: the objective function is as follows:
Figure FDA0001726088930000021
in the formula: u is a membership matrix constructed based on the load curve after per unit processing; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; dikIth cooling, heating and power supply and demand data sample xiDistance from class k center; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity;
wherein d isikCalculated as follows:
Figure FDA0001726088930000022
in the formula, m is the characteristic number of the sample; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; and k is the serial number of the clustering center.
7. The method for regulating and controlling the comprehensive energy system at the user side of claim 5, wherein the membership matrix is constructed according to the following formula:
Figure FDA0001726088930000023
in the formula uikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; i is the serial number of the data sample of the supply and demand of the cold, heat and electricity; k is a clustering center number; dikData sample x for the ith cooling, heating and power supply and demandiDistance from class k center; r is the number of iterations; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity.
8. The method according to claim 5, wherein the clustering center is constructed according to the following formula:
Figure FDA0001726088930000024
in the formula, vk (r+1)Is a clustering center; n is a data sample of cooling, heating and power supply and demand; u. ofikData x for supply and demand of cold, heat and electricityiFor class AkDegree of membership of; r is the number of iterations; k is a clustering center number; i, serial number of data samples of cooling, heating and power supply and demand; b is a weighting parameter, and the value range is more than or equal to 1 and less than or equal to infinity.
9. The method according to claim 1, wherein the calculating based on the regulation period by using a fuzzy clustering algorithm to obtain the energy utilization rating comprises:
based on the energy use grading number of the comprehensive energy system in each regulation and control time period as the clustering number, constructing a membership matrix and a clustering center based on the load curve after per unit processing, and performing iterative computation;
and grading the energy utilization which belongs to the same clustering center and is used as the comprehensive energy system.
10. The method as claimed in claim 9, wherein the energy classification of the integrated energy system comprises: a base stage, an intermediate stage and a peak shaving stage;
the basic level is as follows: a portion not greater than a given first energy level on the per-unit processed load curve in each of the regulation and control periods;
the base peak shaving level is as follows: a part greater than a given second performance level on the per-unit processed load curve in each of the regulation periods;
the remaining part is the peak shaver stage.
11. The method for controlling the comprehensive energy system at the user side according to claim 10, wherein the controlling based on the controlling period and the energy utilization grading and combined with the energy value label sequencing of the energy equipment comprises:
acquiring an output priority sequence of the energy equipment based on the energy value label sequencing;
and carrying out energy supply and demand balance regulation and control through the output priority sequence, the regulation and control time period and the energy utilization grading of the energy equipment.
12. The method for regulating and controlling the comprehensive energy system at the user side according to claim 11, wherein the obtaining of the priority order of the output of the energy devices based on the energy value label sorting comprises:
converting the energy equipment into quantitative indexes based on the sequencing sequence of the energy value labels, and sequencing the energy equipment from large to small according to the quantitative indexes;
calculating the sequencing weighted output level of the energy equipment according to the output level and the quantitative index of the energy equipment;
and sequencing the output priority sequence of the energy equipment according to the sequencing weighted output level of the energy equipment from large to small.
13. The method for controlling the integrated energy system at the user side according to claim 11, wherein the controlling of the energy supply and demand balance through the priority order of output, the control period and the energy utilization grading of the energy equipment comprises:
regulating and controlling the load demand of the basic level through the output priority sequence of the energy equipment;
after the load requirements of the base level are regulated, regulating and controlling the load requirements of the middle level by energy equipment with residual capacity in the energy equipment according to the output priority sequence of the energy equipment;
and after the load requirement of the middle level is regulated, the load requirement of the peak regulation level is balanced by adopting the peak regulation capacity of the reserved peak regulation equipment, and/or the load requirement of the peak regulation level is regulated and controlled by adopting energy equipment with residual capacity according to the output priority sequence of the energy equipment.
14. A hierarchical regulation system for a customer-side integrated energy system, the system comprising:
a dividing module: the system is used for dividing the regulation period into regulation time periods according to the load energy demand of the user side and the time sequence rule;
a grading module: the comprehensive energy resource system is used for setting different grading standards by adopting a fuzzy clustering method in the regulation and control time period and grading the comprehensive energy resource system;
a regulation module: and the system is used for performing hierarchical supply and demand balance regulation and control on the comprehensive energy system according to the obtained energy value label sequence of the energy equipment in the regulation and control time period.
15. The system of claim 14, wherein the partitioning module comprises:
a judgment submodule: the energy consumption judging module is used for judging whether the user side load energy consumption requirement has a remarkable characteristic or not;
a first molecular dividing module: used for dividing regulation and control time intervals according to the significant characteristics;
a second partitioning submodule: the method is used for dividing the regulation and control time period by adopting a fuzzy clustering method.
16. The system of claim 14, wherein the classification module comprises:
a sorting submodule: the power utilization requirement level is decomposed according to a fuzzy clustering method, and power values corresponding to the power utilization requirement level are sequenced;
grading submodule: and grading the energy utilization requirement of the comprehensive energy system at the user side according to the energy utilization requirement level.
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